# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from src.utils.args import ArgumentGroup

# yapf: disable
parser = argparse.ArgumentParser(__doc__)
model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
model_g.add_arg("ernie_config_path", str, None, "Path to the json file for ernie model config.")
model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
model_g.add_arg("init_pretraining_params", str, None, "Init pre-training params which preforms fine-tuning from. If the arg 'init_checkpoint' has been set, this argument wouldn't be valid.")
model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints.")

model_g.add_arg("is_classify", bool, True, "is_classify")
model_g.add_arg("is_regression", bool, False, "is_regression")
model_g.add_arg("task_id", int, 0, "task id")

train_g = ArgumentGroup(parser, "training", "training options.")
train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.")
train_g.add_arg("learning_rate", float, 5e-5, "Learning rate used to train with warmup.")
train_g.add_arg("lr_scheduler", str, "linear_warmup_decay", "scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay'])
train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.")
train_g.add_arg("warmup_proportion", float, 0.1, "Proportion of training steps to perform linear learning rate warmup for.")
train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.")
train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.")
train_g.add_arg("use_recompute", bool, False, "Whether to use recompute optimizer for training.")
train_g.add_arg("use_mix_precision", bool, False, "Whether to use mix-precision optimizer for training.")
train_g.add_arg("use_cross_batch", bool, False, "Whether to use cross-batch for training.")
train_g.add_arg("use_lamb", bool, False, "Whether to use LambOptimizer for training.")
train_g.add_arg("use_dynamic_loss_scaling", bool, True, "Whether to use dynamic loss scaling.")

train_g.add_arg("test_save", str, "./checkpoints/test_result", "test_save")
train_g.add_arg("metric", str, "simple_accuracy", "metric")
train_g.add_arg("incr_every_n_steps", int, 100, "Increases loss scaling every n consecutive.")
train_g.add_arg("decr_every_n_nan_or_inf", int, 2, "Decreases loss scaling every n accumulated steps with nan or inf gradients.")
train_g.add_arg("incr_ratio", float, 2.0, "The multiplier to use when increasing the loss scaling.")
train_g.add_arg("decr_ratio", float, 0.8, "The less-than-one-multiplier to use when decreasing.")

log_g = ArgumentGroup(parser, "logging", "logging related.")
log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
log_g.add_arg("verbose", bool, False, "Whether to output verbose log.")

data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
data_g.add_arg("tokenizer", str, "FullTokenizer", "ATTENTION: the INPUT must be splited by Word with blank while using SentencepieceTokenizer or WordsegTokenizer")
data_g.add_arg("train_set", str, None, "Path to training data.")
data_g.add_arg("test_set", str, None, "Path to test data.")
data_g.add_arg("dev_set", str, None, "Path to validation data.")
data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest sequence.")
data_g.add_arg("q_max_seq_len", int, 32, "Number of words of the longest sequence.")
data_g.add_arg("p_max_seq_len", int, 256, "Number of words of the longest sequence.")
data_g.add_arg("train_data_size", int, 0, "Number of training data's total examples. Set for distribute.")
data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training. see also --in_tokens.")
data_g.add_arg("predict_batch_size", int, None, "Total examples' number in batch for predict. see also --in_tokens.")
data_g.add_arg("in_tokens", bool, False, "If set, the batch size will be the maximum number of tokens in one batch. Otherwise, it will be the maximum number of examples in one batch.")
data_g.add_arg("do_lower_case", bool, True, "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
data_g.add_arg("random_seed", int, None, "Random seed.")
data_g.add_arg("label_map_config", str, None, "label_map_path.")
data_g.add_arg("num_labels", int, 2, "label number")
data_g.add_arg("diagnostic", str, None, "GLUE Diagnostic Dataset")
data_g.add_arg("diagnostic_save", str, None, "GLUE Diagnostic save f")
data_g.add_arg("max_query_length", int, 64, "Max query length.")
data_g.add_arg("max_answer_length", int, 100, "Max answer length.")
data_g.add_arg("doc_stride", int, 128, "When splitting up a long document into chunks, how much stride to take between chunks.")
data_g.add_arg("n_best_size", int, 20, "The total number of n-best predictions to generate in the nbest_predictions.json output file.")
data_g.add_arg("chunk_scheme", type=str, default="IOB", choices=["IO", "IOB", "IOE", "IOBES"], help="chunk scheme")

run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
run_type_g.add_arg("is_distributed", bool, False, "If set, then start distributed training.")
run_type_g.add_arg("use_fast_executor", bool, False, "If set, use fast parallel executor (in experiment).")
run_type_g.add_arg("num_iteration_per_drop_scope", int, 10, "Iteration intervals to drop scope.")
run_type_g.add_arg("do_train", bool, True, "Whether to perform training.")
run_type_g.add_arg("do_val", bool, True, "Whether to perform evaluation on dev data set.")
run_type_g.add_arg("do_test", bool, True, "Whether to perform evaluation on test data set.")
run_type_g.add_arg("output_item", int, 3, "Test output format.")
run_type_g.add_arg("output_file_name", str, None, "Test output file name")
run_type_g.add_arg("test_data_cnt", int, 1110000 , "total cnt of testset")
run_type_g.add_arg("use_multi_gpu_test", bool, False, "Whether to perform evaluation using multiple gpu cards")
run_type_g.add_arg("metrics", bool, True, "Whether to perform evaluation on test data set.")
run_type_g.add_arg("shuffle", bool, True, "")
run_type_g.add_arg("for_cn", bool, False, "model train for cn or for other langs.")
